CS-500: Bayesian Reinforcement Learning

Description

The purpose of this seminar is to meet weekly and discuss research
papers in Bayesian machine learning, with a special focus on
reinforcement learning (RL). We will focus on three types of papers.
The first type will consist of recent work that provides a good
background on Bayesian methods as applied in machine learning:
Dirichlet and Gaussian processes, infinite HMMs, hierarchical Bayesian
models, etc. The second type of papers will consist of applications of
Bayesian methods specifically to RL. The third type will involve work
on Bayesian models in the intersections of computer and cognitive
science. It will be assumed that participants have been exposed to
basic concepts in reinforcement learning. All enrolled students will
be expected to lead at least one discussion.

Meeting Schedule

09/09/08: First meeting (welcome back!). Carlos will do
some introduction, then Chris will be presenting an introduction to
Bayes, conjugate priors and Gaussian process regression.